Smart IoT Network Based Convolutional Recurrent Neural Network With Element-Wise Prediction System

Al-Jamali, Nadia Adnan Shiltagh; Al-Raweshidy, Hamed S.

doi:10.1109/access.2021.3068610

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…The paper [18] introduces an Intelligent-IoT (I-IoT) architecture tailored for healthcare applications, anchored on a deep learning Artificial Intelligent System (AIS) that acts as a controller. This AIS is based on the EG-CRNN structure, a hybrid of DL-RNN and EleAttG, aiming to optimize packet flow by intelligently selecting cluster heads and their members.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Optimized Routing Technique With Distributed SDN-AI to Large Scale I-IoT Networks

Udayaprasad,

Shreyas,

Srinidhi

et al. 2024

IEEE Access

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Effective research has been aimed at increasing the distributed compute dependent Software Define Network (SDN) with high-level Intelligent -Internet of Things (I-IoT). Wireless sensor networks come with a set of resource restrictions. Still, only a few functions are often configured such as energy restraint and the concerted demands that are vital for IoT application routing performance. A major technique for solving the expansion of network scalability by applying Mobile Sink (MS). The construction of data transmission optimal path, the detection of an optimal set data-gathering points O DG and MS scheduled with dynamic networks for energy-efficient techniques, that the network's lifetime in enormous complications, principally in large-scale IoT networks. The research work proposes an Research Objective: i) Develop an energy-efficient routing technique for large-scale I-IoT networks within a cloud-based SDN system. ii) Optimize network scalability, lower-level routing, and load balancing using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The prime aim of cloud-based SDN with AI is to determine: a lower level routing in the perception layer, a load-balanced Cluster Table (CT), an optimal O DG points, and MS optimal paths O MSpath . The main contribution of proposed routing is i) Energy Minimization (EM): The proposed routing minimizes energy dissemination by the Cluster Head (CH) in critical conditions (EM-CH). ii) Enhanced Energy Balance (EEB): The ECbased SDN, considering both Optimal Data-Gathering (O DG ) and Mobile Sink (MS) advancements, achieves enhanced energy balance during network routing (EEB-SDN). Research results validate the proposed model stability that improves the network lifetime up to 63%, the energy usage in the network is reduced up to 78%, the high volume data loaded to the MS up to 95%, and the delay of the O MSpath by 69% when compared with various model.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Optimized Routing Technique With Distributed SDN-AI to Large Scale I-IoT Networks

Udayaprasad,

Shreyas,

Srinidhi

et al. 2024

IEEE Access

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“…In this paper [47], writers suggest an artificially intelligent system for reducing the congestion effects in traffic load in an Intelligent Internet of Things network based on a DL Convolutional Recurrent Neural Network with a modified Element‐wise Attention Gate. The invisible layer of the modified Element‐wise Attention Gate structure has self‐feedback to increase its LSTM.…”

Section: Examples Of Recent Work In Iotmentioning

confidence: 99%

“…The artificially intelligent system is created for the next step ahead of traffic estimation and clustering of the network. This study shows that researchers using DL and ML methods can effectively handle IoT problems [47].…”

Section: Examples Of Recent Work In Iotmentioning

confidence: 99%

Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Gheisari

Ebrahimzadeh

Rahimi

et al. 2023

CAAI Trans on Intel Tech

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Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on.

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“…To reduce congestion in IoT networks, R Hassan et al presented a unique adaptive technique in [54]. The adaptive nature of the mechanism, which can respond to the transmitted traffic and the changing conditions of the network, makes this technology highly novel.…”

Section: Conventional Algorithms For Congestion Mitigation In Wsnmentioning

confidence: 99%

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

Umar,

Khalid,

Ali

et al. 2023

Applied Sciences

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The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

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Smart IoT Network Based Convolutional Recurrent Neural Network With Element-Wise Prediction System

Cited by 11 publications

References 35 publications

Energy Efficient Optimized Routing Technique With Distributed SDN-AI to Large Scale I-IoT Networks

Energy Efficient Optimized Routing Technique With Distributed SDN-AI to Large Scale I-IoT Networks

Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

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